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Abstract:This study investigates heart rate (HR) and heart rate variability (HRV) responses to physical activity in breast cancer survivors using wearable electrocardiogram (ECG) data collected in real-world settings. Reliable HRV analysis in such environments is challenging due to motion artifacts and activity-related signal degradation. To address this, we use an approach that combines accelerometer and gyroscope data for activity intensity segmentation (light, moderate, vigorous) with a robust ECG processing pipeline incorporating R-peak detection and annotation-free signal quality assessment. Because vigorous activity produced unreliable HRV estimates, analyses focused on light and moderate activity levels. Using 30~s, 1~min, and 2~min windows, HR and HRV metrics were computed and compared between breast cancer survivors and healthy controls. Cancer survivors consistently exhibited elevated HR and reduced HRV across activity levels. During light activity, HR increased from 95.7~bpm in controls to 103.4~bpm in cancer survivors. Differences became more pronounced during moderate activity, where RMSSD decreased from 39.7~ms to 22.1~ms and SDNN from 42.6~ms to 25.1~ms. Statistical analyses showed significant group differences with strong and consistent effects across observations. In addition, the proposed ECG quality assessment framework reliably identified high-quality signal segments, achieving near-perfect valid RR ratios (0.99) without manual annotations. Overall, these findings demonstrate impaired and activity-dependent autonomic regulation in cancer survivors and highlight the importance of motion-aware activity segmentation and robust ECG quality control for accurate physiological monitoring in real-world wearable settings.

Submission history

From: Waltenegus Dargie [view email]
[v1] Mon, 22 Jun 2026 15:10:17 UTC (1,244 KB)